Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations200000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory257.5 MiB
Average record size in memory1.3 KiB

Variable types

Text6
Categorical13
Numeric3
DateTime2

Alerts

Is_Fraud is highly overall correlated with Transaction_Amount and 1 other fieldsHigh correlation
Transaction_Amount is highly overall correlated with Is_FraudHigh correlation
Transaction_Location is highly overall correlated with Is_FraudHigh correlation
Is_Fraud is highly imbalanced (63.4%) Imbalance
Customer_ID has unique values Unique
Customer_Name has unique values Unique
Transaction_ID has unique values Unique
Transaction_Date has unique values Unique

Reproduction

Analysis started2025-02-27 02:21:27.953744
Analysis finished2025-02-27 02:22:17.490265
Duration49.54 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Customer_ID
Text

Unique 

Distinct200000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.8 MiB
2025-02-26T21:22:17.864921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2000000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200000 ?
Unique (%)100.0%

Sample

1st rowCUST000001
2nd rowCUST000002
3rd rowCUST000003
4th rowCUST000004
5th rowCUST000005
ValueCountFrequency (%)
cust199985 1
 
< 0.1%
cust199986 1
 
< 0.1%
cust199987 1
 
< 0.1%
cust199988 1
 
< 0.1%
cust199989 1
 
< 0.1%
cust199990 1
 
< 0.1%
cust199991 1
 
< 0.1%
cust199992 1
 
< 0.1%
cust199993 1
 
< 0.1%
cust199994 1
 
< 0.1%
Other values (199990) 199990
> 99.9%
2025-02-26T21:22:18.297129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 200000
10.0%
U 200000
10.0%
S 200000
10.0%
T 200000
10.0%
1 200000
10.0%
0 199999
10.0%
2 100001
 
5.0%
9 100000
 
5.0%
4 100000
 
5.0%
8 100000
 
5.0%
Other values (4) 400000
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 200000
10.0%
U 200000
10.0%
S 200000
10.0%
T 200000
10.0%
1 200000
10.0%
0 199999
10.0%
2 100001
 
5.0%
9 100000
 
5.0%
4 100000
 
5.0%
8 100000
 
5.0%
Other values (4) 400000
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 200000
10.0%
U 200000
10.0%
S 200000
10.0%
T 200000
10.0%
1 200000
10.0%
0 199999
10.0%
2 100001
 
5.0%
9 100000
 
5.0%
4 100000
 
5.0%
8 100000
 
5.0%
Other values (4) 400000
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 200000
10.0%
U 200000
10.0%
S 200000
10.0%
T 200000
10.0%
1 200000
10.0%
0 199999
10.0%
2 100001
 
5.0%
9 100000
 
5.0%
4 100000
 
5.0%
8 100000
 
5.0%
Other values (4) 400000
20.0%

Customer_Name
Text

Unique 

Distinct200000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size13.6 MiB
2025-02-26T21:22:18.714365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length15
Mean length14.444475
Min length10

Characters and Unicode

Total characters2888895
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200000 ?
Unique (%)100.0%

Sample

1st rowCustomer_1
2nd rowCustomer_2
3rd rowCustomer_3
4th rowCustomer_4
5th rowCustomer_5
ValueCountFrequency (%)
customer_199985 1
 
< 0.1%
customer_199986 1
 
< 0.1%
customer_199987 1
 
< 0.1%
customer_199988 1
 
< 0.1%
customer_199989 1
 
< 0.1%
customer_199990 1
 
< 0.1%
customer_199991 1
 
< 0.1%
customer_199992 1
 
< 0.1%
customer_199993 1
 
< 0.1%
customer_199994 1
 
< 0.1%
Other values (199990) 199990
> 99.9%
2025-02-26T21:22:19.160763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 200000
 
6.9%
u 200000
 
6.9%
s 200000
 
6.9%
t 200000
 
6.9%
o 200000
 
6.9%
m 200000
 
6.9%
e 200000
 
6.9%
r 200000
 
6.9%
_ 200000
 
6.9%
1 200000
 
6.9%
Other values (9) 888895
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2888895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 200000
 
6.9%
u 200000
 
6.9%
s 200000
 
6.9%
t 200000
 
6.9%
o 200000
 
6.9%
m 200000
 
6.9%
e 200000
 
6.9%
r 200000
 
6.9%
_ 200000
 
6.9%
1 200000
 
6.9%
Other values (9) 888895
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2888895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 200000
 
6.9%
u 200000
 
6.9%
s 200000
 
6.9%
t 200000
 
6.9%
o 200000
 
6.9%
m 200000
 
6.9%
e 200000
 
6.9%
r 200000
 
6.9%
_ 200000
 
6.9%
1 200000
 
6.9%
Other values (9) 888895
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2888895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 200000
 
6.9%
u 200000
 
6.9%
s 200000
 
6.9%
t 200000
 
6.9%
o 200000
 
6.9%
m 200000
 
6.9%
e 200000
 
6.9%
r 200000
 
6.9%
_ 200000
 
6.9%
1 200000
 
6.9%
Other values (9) 888895
30.8%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.8 MiB
Female
100232 
Male
99768 

Length

Max length6
Median length6
Mean length5.00232
Min length4

Characters and Unicode

Total characters1000464
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 100232
50.1%
Male 99768
49.9%

Length

2025-02-26T21:22:19.274352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T21:22:19.364021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 100232
50.1%
male 99768
49.9%

Most occurring characters

ValueCountFrequency (%)
e 300232
30.0%
a 200000
20.0%
l 200000
20.0%
F 100232
 
10.0%
m 100232
 
10.0%
M 99768
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 300232
30.0%
a 200000
20.0%
l 200000
20.0%
F 100232
 
10.0%
m 100232
 
10.0%
M 99768
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 300232
30.0%
a 200000
20.0%
l 200000
20.0%
F 100232
 
10.0%
m 100232
 
10.0%
M 99768
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 300232
30.0%
a 200000
20.0%
l 200000
20.0%
F 100232
 
10.0%
m 100232
 
10.0%
M 99768
 
10.0%

Age
Real number (ℝ)

Distinct62
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.46971
Minimum18
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-02-26T21:22:19.466481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q133
median48
Q364
95-th percentile76
Maximum79
Range61
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.896964
Coefficient of variation (CV)0.36924017
Kurtosis-1.1997223
Mean48.46971
Median Absolute Deviation (MAD)15
Skewness0.0007206454
Sum9693942
Variance320.30132
MonotonicityNot monotonic
2025-02-26T21:22:19.616099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 3343
 
1.7%
40 3341
 
1.7%
34 3316
 
1.7%
22 3314
 
1.7%
62 3313
 
1.7%
49 3293
 
1.6%
25 3286
 
1.6%
44 3282
 
1.6%
73 3279
 
1.6%
70 3273
 
1.6%
Other values (52) 166960
83.5%
ValueCountFrequency (%)
18 3261
1.6%
19 3265
1.6%
20 3264
1.6%
21 3184
1.6%
22 3314
1.7%
23 3257
1.6%
24 3162
1.6%
25 3286
1.6%
26 3202
1.6%
27 3188
1.6%
ValueCountFrequency (%)
79 3213
1.6%
78 3216
1.6%
77 3173
1.6%
76 3264
1.6%
75 3239
1.6%
74 3112
1.6%
73 3279
1.6%
72 3213
1.6%
71 3195
1.6%
70 3273
1.6%

State
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.3 MiB
FL
28648 
OH
28631 
NY
28613 
TX
28549 
CA
28538 
Other values (2)
57021 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters400000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCA
2nd rowFL
3rd rowPA
4th rowNY
5th rowOH

Common Values

ValueCountFrequency (%)
FL 28648
14.3%
OH 28631
14.3%
NY 28613
14.3%
TX 28549
14.3%
CA 28538
14.3%
IL 28525
14.3%
PA 28496
14.2%

Length

2025-02-26T21:22:19.734403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T21:22:19.831107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fl 28648
14.3%
oh 28631
14.3%
ny 28613
14.3%
tx 28549
14.3%
ca 28538
14.3%
il 28525
14.3%
pa 28496
14.2%

Most occurring characters

ValueCountFrequency (%)
L 57173
14.3%
A 57034
14.3%
F 28648
7.2%
O 28631
7.2%
H 28631
7.2%
N 28613
7.2%
Y 28613
7.2%
T 28549
7.1%
X 28549
7.1%
C 28538
7.1%
Other values (2) 57021
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 400000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 57173
14.3%
A 57034
14.3%
F 28648
7.2%
O 28631
7.2%
H 28631
7.2%
N 28613
7.2%
Y 28613
7.2%
T 28549
7.1%
X 28549
7.1%
C 28538
7.1%
Other values (2) 57021
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 400000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 57173
14.3%
A 57034
14.3%
F 28648
7.2%
O 28631
7.2%
H 28631
7.2%
N 28613
7.2%
Y 28613
7.2%
T 28549
7.1%
X 28549
7.1%
C 28538
7.1%
Other values (2) 57021
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 400000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 57173
14.3%
A 57034
14.3%
F 28648
7.2%
O 28631
7.2%
H 28631
7.2%
N 28613
7.2%
Y 28613
7.2%
T 28549
7.1%
X 28549
7.1%
C 28538
7.1%
Other values (2) 57021
14.3%

City
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.4 MiB
Chicago
28762 
Houston
28640 
Philadelphia
28583 
Dallas
28578 
Los Angeles
28507 
Other values (2)
56930 

Length

Max length12
Median length8
Mean length7.999245
Min length5

Characters and Unicode

Total characters1599849
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLos Angeles
2nd rowNew York
3rd rowDallas
4th rowChicago
5th rowPhiladelphia

Common Values

ValueCountFrequency (%)
Chicago 28762
14.4%
Houston 28640
14.3%
Philadelphia 28583
14.3%
Dallas 28578
14.3%
Los Angeles 28507
14.3%
Miami 28482
14.2%
New York 28448
14.2%

Length

2025-02-26T21:22:19.973204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T21:22:20.082065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
chicago 28762
11.2%
houston 28640
11.1%
philadelphia 28583
11.1%
dallas 28578
11.1%
los 28507
11.1%
angeles 28507
11.1%
miami 28482
11.1%
new 28448
11.1%
york 28448
11.1%

Most occurring characters

ValueCountFrequency (%)
a 171566
 
10.7%
o 142997
 
8.9%
i 142892
 
8.9%
l 142829
 
8.9%
s 114232
 
7.1%
e 114045
 
7.1%
h 85928
 
5.4%
g 57269
 
3.6%
n 57147
 
3.6%
56955
 
3.6%
Other values (18) 513989
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1599849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 171566
 
10.7%
o 142997
 
8.9%
i 142892
 
8.9%
l 142829
 
8.9%
s 114232
 
7.1%
e 114045
 
7.1%
h 85928
 
5.4%
g 57269
 
3.6%
n 57147
 
3.6%
56955
 
3.6%
Other values (18) 513989
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1599849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 171566
 
10.7%
o 142997
 
8.9%
i 142892
 
8.9%
l 142829
 
8.9%
s 114232
 
7.1%
e 114045
 
7.1%
h 85928
 
5.4%
g 57269
 
3.6%
n 57147
 
3.6%
56955
 
3.6%
Other values (18) 513989
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1599849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 171566
 
10.7%
o 142997
 
8.9%
i 142892
 
8.9%
l 142829
 
8.9%
s 114232
 
7.1%
e 114045
 
7.1%
h 85928
 
5.4%
g 57269
 
3.6%
n 57147
 
3.6%
56955
 
3.6%
Other values (18) 513989
32.1%

Bank_Branch
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.4 MiB
Branch A
50274 
Branch C
50027 
Branch D
49850 
Branch B
49849 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1600000
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBranch B
2nd rowBranch C
3rd rowBranch A
4th rowBranch A
5th rowBranch B

Common Values

ValueCountFrequency (%)
Branch A 50274
25.1%
Branch C 50027
25.0%
Branch D 49850
24.9%
Branch B 49849
24.9%

Length

2025-02-26T21:22:20.222196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T21:22:20.460963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
branch 200000
50.0%
a 50274
 
12.6%
c 50027
 
12.5%
d 49850
 
12.5%
b 49849
 
12.5%

Most occurring characters

ValueCountFrequency (%)
B 249849
15.6%
r 200000
12.5%
a 200000
12.5%
n 200000
12.5%
c 200000
12.5%
h 200000
12.5%
200000
12.5%
A 50274
 
3.1%
C 50027
 
3.1%
D 49850
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1600000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 249849
15.6%
r 200000
12.5%
a 200000
12.5%
n 200000
12.5%
c 200000
12.5%
h 200000
12.5%
200000
12.5%
A 50274
 
3.1%
C 50027
 
3.1%
D 49850
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1600000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 249849
15.6%
r 200000
12.5%
a 200000
12.5%
n 200000
12.5%
c 200000
12.5%
h 200000
12.5%
200000
12.5%
A 50274
 
3.1%
C 50027
 
3.1%
D 49850
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1600000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 249849
15.6%
r 200000
12.5%
a 200000
12.5%
n 200000
12.5%
c 200000
12.5%
h 200000
12.5%
200000
12.5%
A 50274
 
3.1%
C 50027
 
3.1%
D 49850
 
3.1%

Account_Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
Savings
119763 
Checking
60167 
Business
20070 

Length

Max length8
Median length7
Mean length7.401185
Min length7

Characters and Unicode

Total characters1480237
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChecking
2nd rowSavings
3rd rowSavings
4th rowSavings
5th rowSavings

Common Values

ValueCountFrequency (%)
Savings 119763
59.9%
Checking 60167
30.1%
Business 20070
 
10.0%

Length

2025-02-26T21:22:20.581785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T21:22:20.662216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
savings 119763
59.9%
checking 60167
30.1%
business 20070
 
10.0%

Most occurring characters

ValueCountFrequency (%)
n 200000
13.5%
i 200000
13.5%
s 179973
12.2%
g 179930
12.2%
a 119763
8.1%
S 119763
8.1%
v 119763
8.1%
e 80237
5.4%
C 60167
 
4.1%
h 60167
 
4.1%
Other values (4) 160474
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1480237
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 200000
13.5%
i 200000
13.5%
s 179973
12.2%
g 179930
12.2%
a 119763
8.1%
S 119763
8.1%
v 119763
8.1%
e 80237
5.4%
C 60167
 
4.1%
h 60167
 
4.1%
Other values (4) 160474
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1480237
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 200000
13.5%
i 200000
13.5%
s 179973
12.2%
g 179930
12.2%
a 119763
8.1%
S 119763
8.1%
v 119763
8.1%
e 80237
5.4%
C 60167
 
4.1%
h 60167
 
4.1%
Other values (4) 160474
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1480237
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 200000
13.5%
i 200000
13.5%
s 179973
12.2%
g 179930
12.2%
a 119763
8.1%
S 119763
8.1%
v 119763
8.1%
e 80237
5.4%
C 60167
 
4.1%
h 60167
 
4.1%
Other values (4) 160474
10.8%

Transaction_ID
Text

Unique 

Distinct200000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size13.2 MiB
2025-02-26T21:22:20.948823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2400000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200000 ?
Unique (%)100.0%

Sample

1st rowTXN000000001
2nd rowTXN000000002
3rd rowTXN000000003
4th rowTXN000000004
5th rowTXN000000005
ValueCountFrequency (%)
txn000199985 1
 
< 0.1%
txn000199986 1
 
< 0.1%
txn000199987 1
 
< 0.1%
txn000199988 1
 
< 0.1%
txn000199989 1
 
< 0.1%
txn000199990 1
 
< 0.1%
txn000199991 1
 
< 0.1%
txn000199992 1
 
< 0.1%
txn000199993 1
 
< 0.1%
txn000199994 1
 
< 0.1%
Other values (199990) 199990
> 99.9%
2025-02-26T21:22:21.316429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 799999
33.3%
T 200000
 
8.3%
X 200000
 
8.3%
N 200000
 
8.3%
1 200000
 
8.3%
2 100001
 
4.2%
9 100000
 
4.2%
4 100000
 
4.2%
8 100000
 
4.2%
3 100000
 
4.2%
Other values (3) 300000
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2400000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 799999
33.3%
T 200000
 
8.3%
X 200000
 
8.3%
N 200000
 
8.3%
1 200000
 
8.3%
2 100001
 
4.2%
9 100000
 
4.2%
4 100000
 
4.2%
8 100000
 
4.2%
3 100000
 
4.2%
Other values (3) 300000
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2400000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 799999
33.3%
T 200000
 
8.3%
X 200000
 
8.3%
N 200000
 
8.3%
1 200000
 
8.3%
2 100001
 
4.2%
9 100000
 
4.2%
4 100000
 
4.2%
8 100000
 
4.2%
3 100000
 
4.2%
Other values (3) 300000
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2400000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 799999
33.3%
T 200000
 
8.3%
X 200000
 
8.3%
N 200000
 
8.3%
1 200000
 
8.3%
2 100001
 
4.2%
9 100000
 
4.2%
4 100000
 
4.2%
8 100000
 
4.2%
3 100000
 
4.2%
Other values (3) 300000
 
12.5%

Transaction_Date
Date

Unique 

Distinct200000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2023-01-01 00:00:00
Maximum2023-05-19 21:19:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-26T21:22:21.450010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-26T21:22:21.613649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1440
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2025-02-26 00:00:00
Maximum2025-02-26 23:59:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-26T21:22:21.764505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-26T21:22:21.909746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Transaction_Amount
Real number (ℝ)

High correlation 

Distinct185461
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.03262
Minimum5
Maximum1897.8694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-02-26T21:22:22.055031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q131.508303
median52.831014
Q375.871259
95-th percentile630.38974
Maximum1897.8694
Range1892.8694
Interquartile range (IQR)44.362956

Descriptive statistics

Standard deviation210.89496
Coefficient of variation (CV)2.0078996
Kurtosis15.452889
Mean105.03262
Median Absolute Deviation (MAD)22.128433
Skewness3.9231113
Sum21006524
Variance44476.685
MonotonicityNot monotonic
2025-02-26T21:22:22.234016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 12341
 
6.2%
500 2200
 
1.1%
83.03462405 1
 
< 0.1%
51.52127843 1
 
< 0.1%
74.89715577 1
 
< 0.1%
642.9258356 1
 
< 0.1%
998.7898949 1
 
< 0.1%
772.0799109 1
 
< 0.1%
974.7373898 1
 
< 0.1%
990.3640153 1
 
< 0.1%
Other values (185451) 185451
92.7%
ValueCountFrequency (%)
5 12341
6.2%
5.000461214 1
 
< 0.1%
5.000605258 1
 
< 0.1%
5.002293223 1
 
< 0.1%
5.006229507 1
 
< 0.1%
5.007303913 1
 
< 0.1%
5.008446896 1
 
< 0.1%
5.010560559 1
 
< 0.1%
5.010751688 1
 
< 0.1%
5.010990974 1
 
< 0.1%
ValueCountFrequency (%)
1897.869419 1
< 0.1%
1834.433622 1
< 0.1%
1820.311895 1
< 0.1%
1809.659966 1
< 0.1%
1797.684375 1
< 0.1%
1787.498 1
< 0.1%
1779.59386 1
< 0.1%
1772.913102 1
< 0.1%
1764.350422 1
< 0.1%
1760.67967 1
< 0.1%
Distinct999
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
2025-02-26T21:22:22.492474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.89141
Min length9

Characters and Unicode

Total characters2178282
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMERCHANT839
2nd rowMERCHANT50
3rd rowMERCHANT453
4th rowMERCHANT231
5th rowMERCHANT664
ValueCountFrequency (%)
merchant229 270
 
0.1%
merchant496 247
 
0.1%
merchant146 247
 
0.1%
merchant302 239
 
0.1%
merchant716 237
 
0.1%
merchant225 235
 
0.1%
merchant350 234
 
0.1%
merchant597 234
 
0.1%
merchant675 234
 
0.1%
merchant490 233
 
0.1%
Other values (989) 197590
98.8%
2025-02-26T21:22:22.850212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 200000
9.2%
E 200000
9.2%
R 200000
9.2%
C 200000
9.2%
H 200000
9.2%
A 200000
9.2%
N 200000
9.2%
T 200000
9.2%
2 60436
 
2.8%
5 60320
 
2.8%
Other values (8) 457526
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2178282
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 200000
9.2%
E 200000
9.2%
R 200000
9.2%
C 200000
9.2%
H 200000
9.2%
A 200000
9.2%
N 200000
9.2%
T 200000
9.2%
2 60436
 
2.8%
5 60320
 
2.8%
Other values (8) 457526
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2178282
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 200000
9.2%
E 200000
9.2%
R 200000
9.2%
C 200000
9.2%
H 200000
9.2%
A 200000
9.2%
N 200000
9.2%
T 200000
9.2%
2 60436
 
2.8%
5 60320
 
2.8%
Other values (8) 457526
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2178282
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 200000
9.2%
E 200000
9.2%
R 200000
9.2%
C 200000
9.2%
H 200000
9.2%
A 200000
9.2%
N 200000
9.2%
T 200000
9.2%
2 60436
 
2.8%
5 60320
 
2.8%
Other values (8) 457526
21.0%

Transaction_Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.1 MiB
Online
80142 
POS
79974 
ATM Withdrawal
39884 

Length

Max length14
Median length6
Mean length6.39575
Min length3

Characters and Unicode

Total characters1279150
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOnline
2nd rowATM Withdrawal
3rd rowATM Withdrawal
4th rowATM Withdrawal
5th rowOnline

Common Values

ValueCountFrequency (%)
Online 80142
40.1%
POS 79974
40.0%
ATM Withdrawal 39884
19.9%

Length

2025-02-26T21:22:23.009557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T21:22:23.100707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
online 80142
33.4%
pos 79974
33.3%
atm 39884
16.6%
withdrawal 39884
16.6%

Most occurring characters

ValueCountFrequency (%)
n 160284
12.5%
O 160116
12.5%
l 120026
 
9.4%
i 120026
 
9.4%
e 80142
 
6.3%
P 79974
 
6.3%
S 79974
 
6.3%
a 79768
 
6.2%
T 39884
 
3.1%
A 39884
 
3.1%
Other values (8) 319072
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1279150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 160284
12.5%
O 160116
12.5%
l 120026
 
9.4%
i 120026
 
9.4%
e 80142
 
6.3%
P 79974
 
6.3%
S 79974
 
6.3%
a 79768
 
6.2%
T 39884
 
3.1%
A 39884
 
3.1%
Other values (8) 319072
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1279150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 160284
12.5%
O 160116
12.5%
l 120026
 
9.4%
i 120026
 
9.4%
e 80142
 
6.3%
P 79974
 
6.3%
S 79974
 
6.3%
a 79768
 
6.2%
T 39884
 
3.1%
A 39884
 
3.1%
Other values (8) 319072
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1279150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 160284
12.5%
O 160116
12.5%
l 120026
 
9.4%
i 120026
 
9.4%
e 80142
 
6.3%
P 79974
 
6.3%
S 79974
 
6.3%
a 79768
 
6.2%
T 39884
 
3.1%
A 39884
 
3.1%
Other values (8) 319072
24.9%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.6 MiB
Travel
35929 
Luxury
35605 
Online Services
35545 
Electronics
31180 
Grocery
30962 

Length

Max length15
Median length11
Mean length8.841625
Min length6

Characters and Unicode

Total characters1768325
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClothing
2nd rowLuxury
3rd rowTravel
4th rowClothing
5th rowElectronics

Common Values

ValueCountFrequency (%)
Travel 35929
18.0%
Luxury 35605
17.8%
Online Services 35545
17.8%
Electronics 31180
15.6%
Grocery 30962
15.5%
Clothing 30779
15.4%

Length

2025-02-26T21:22:23.209138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T21:22:23.347191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
travel 35929
15.3%
luxury 35605
15.1%
online 35545
15.1%
services 35545
15.1%
electronics 31180
13.2%
grocery 30962
13.1%
clothing 30779
13.1%

Most occurring characters

ValueCountFrequency (%)
e 204706
 
11.6%
r 200183
 
11.3%
l 133433
 
7.5%
n 133049
 
7.5%
i 133049
 
7.5%
c 128867
 
7.3%
o 92921
 
5.3%
v 71474
 
4.0%
u 71210
 
4.0%
s 66725
 
3.8%
Other values (14) 532708
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1768325
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 204706
 
11.6%
r 200183
 
11.3%
l 133433
 
7.5%
n 133049
 
7.5%
i 133049
 
7.5%
c 128867
 
7.3%
o 92921
 
5.3%
v 71474
 
4.0%
u 71210
 
4.0%
s 66725
 
3.8%
Other values (14) 532708
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1768325
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 204706
 
11.6%
r 200183
 
11.3%
l 133433
 
7.5%
n 133049
 
7.5%
i 133049
 
7.5%
c 128867
 
7.3%
o 92921
 
5.3%
v 71474
 
4.0%
u 71210
 
4.0%
s 66725
 
3.8%
Other values (14) 532708
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1768325
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 204706
 
11.6%
r 200183
 
11.3%
l 133433
 
7.5%
n 133049
 
7.5%
i 133049
 
7.5%
c 128867
 
7.3%
o 92921
 
5.3%
v 71474
 
4.0%
u 71210
 
4.0%
s 66725
 
3.8%
Other values (14) 532708
30.1%

Account_Balance
Real number (ℝ)

Distinct198734
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4995.8658
Minimum0
Maximum13631.898
Zeros1267
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-02-26T21:22:23.530281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1690.519
Q13646.6209
median4987.4005
Q36343.9981
95-th percentile8296.04
Maximum13631.898
Range13631.898
Interquartile range (IQR)2697.3772

Descriptive statistics

Standard deviation1990.9146
Coefficient of variation (CV)0.39851242
Kurtosis-0.11582181
Mean4995.8658
Median Absolute Deviation (MAD)1348.3527
Skewness0.044220137
Sum9.9917316 × 108
Variance3963740.8
MonotonicityNot monotonic
2025-02-26T21:22:23.720870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1267
 
0.6%
4070.596903 1
 
< 0.1%
4790.814115 1
 
< 0.1%
2799.823637 1
 
< 0.1%
3511.964463 1
 
< 0.1%
6325.143822 1
 
< 0.1%
4943.596395 1
 
< 0.1%
5517.654152 1
 
< 0.1%
6330.928087 1
 
< 0.1%
3392.716244 1
 
< 0.1%
Other values (198724) 198724
99.4%
ValueCountFrequency (%)
0 1267
0.6%
0.002169337859 1
 
< 0.1%
0.4378865496 1
 
< 0.1%
2.004019109 1
 
< 0.1%
3.956628034 1
 
< 0.1%
4.046878393 1
 
< 0.1%
5.956477747 1
 
< 0.1%
6.417498548 1
 
< 0.1%
6.990873221 1
 
< 0.1%
6.991436561 1
 
< 0.1%
ValueCountFrequency (%)
13631.89815 1
< 0.1%
13324.62493 1
< 0.1%
13305.5696 1
< 0.1%
13030.04863 1
< 0.1%
12991.81541 1
< 0.1%
12908.76152 1
< 0.1%
12879.6356 1
< 0.1%
12766.36252 1
< 0.1%
12732.06282 1
< 0.1%
12624.48431 1
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
Web
53687 
ATM
53499 
POS Terminal
46436 
Mobile
46378 

Length

Max length12
Median length3
Mean length5.78529
Min length3

Characters and Unicode

Total characters1157058
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOS Terminal
2nd rowWeb
3rd rowPOS Terminal
4th rowPOS Terminal
5th rowWeb

Common Values

ValueCountFrequency (%)
Web 53687
26.8%
ATM 53499
26.7%
POS Terminal 46436
23.2%
Mobile 46378
23.2%

Length

2025-02-26T21:22:23.906107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T21:22:23.997212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
web 53687
21.8%
atm 53499
21.7%
pos 46436
18.8%
terminal 46436
18.8%
mobile 46378
18.8%

Most occurring characters

ValueCountFrequency (%)
e 146501
12.7%
b 100065
 
8.6%
T 99935
 
8.6%
M 99877
 
8.6%
l 92814
 
8.0%
i 92814
 
8.0%
W 53687
 
4.6%
A 53499
 
4.6%
P 46436
 
4.0%
S 46436
 
4.0%
Other values (7) 324994
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1157058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 146501
12.7%
b 100065
 
8.6%
T 99935
 
8.6%
M 99877
 
8.6%
l 92814
 
8.0%
i 92814
 
8.0%
W 53687
 
4.6%
A 53499
 
4.6%
P 46436
 
4.0%
S 46436
 
4.0%
Other values (7) 324994
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1157058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 146501
12.7%
b 100065
 
8.6%
T 99935
 
8.6%
M 99877
 
8.6%
l 92814
 
8.0%
i 92814
 
8.0%
W 53687
 
4.6%
A 53499
 
4.6%
P 46436
 
4.0%
S 46436
 
4.0%
Other values (7) 324994
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1157058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 146501
12.7%
b 100065
 
8.6%
T 99935
 
8.6%
M 99877
 
8.6%
l 92814
 
8.0%
i 92814
 
8.0%
W 53687
 
4.6%
A 53499
 
4.6%
P 46436
 
4.0%
S 46436
 
4.0%
Other values (7) 324994
28.1%

Transaction_Location
Categorical

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.3 MiB
OH
26773 
PA
26689 
IL
26645 
CA
26556 
TX
26489 
Other values (5)
66848 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters400000
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCA
2nd rowTX
3rd rowIL
4th rowCA
5th rowPA

Common Values

ValueCountFrequency (%)
OH 26773
13.4%
PA 26689
13.3%
IL 26645
13.3%
CA 26556
13.3%
TX 26489
13.2%
FL 26434
13.2%
NY 26414
13.2%
Hi 4721
 
2.4%
Un 4641
 
2.3%
Of 4638
 
2.3%

Length

2025-02-26T21:22:24.118248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T21:22:24.252976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
oh 26773
13.4%
pa 26689
13.3%
il 26645
13.3%
ca 26556
13.3%
tx 26489
13.2%
fl 26434
13.2%
ny 26414
13.2%
hi 4721
 
2.4%
un 4641
 
2.3%
of 4638
 
2.3%

Most occurring characters

ValueCountFrequency (%)
A 53245
13.3%
L 53079
13.3%
H 31494
7.9%
O 31411
7.9%
P 26689
 
6.7%
I 26645
 
6.7%
C 26556
 
6.6%
T 26489
 
6.6%
X 26489
 
6.6%
F 26434
 
6.6%
Other values (6) 71469
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 400000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 53245
13.3%
L 53079
13.3%
H 31494
7.9%
O 31411
7.9%
P 26689
 
6.7%
I 26645
 
6.7%
C 26556
 
6.6%
T 26489
 
6.6%
X 26489
 
6.6%
F 26434
 
6.6%
Other values (6) 71469
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 400000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 53245
13.3%
L 53079
13.3%
H 31494
7.9%
O 31411
7.9%
P 26689
 
6.7%
I 26645
 
6.7%
C 26556
 
6.6%
T 26489
 
6.6%
X 26489
 
6.6%
F 26434
 
6.6%
Other values (6) 71469
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 400000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 53245
13.3%
L 53079
13.3%
H 31494
7.9%
O 31411
7.9%
P 26689
 
6.7%
I 26645
 
6.7%
C 26556
 
6.6%
T 26489
 
6.6%
X 26489
 
6.6%
F 26434
 
6.6%
Other values (6) 71469
17.9%

Device_Type
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
Windows PC
38104 
ATM Machine
37942 
Mac
31173 
Android
31016 
Linux
30969 

Length

Max length11
Median length7
Mean length7.24327
Min length3

Characters and Unicode

Total characters1448654
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWindows PC
2nd rowAndroid
3rd rowWindows PC
4th rowWindows PC
5th rowLinux

Common Values

ValueCountFrequency (%)
Windows PC 38104
19.1%
ATM Machine 37942
19.0%
Mac 31173
15.6%
Android 31016
15.5%
Linux 30969
15.5%
iPhone 30796
15.4%

Length

2025-02-26T21:22:24.429443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T21:22:24.527982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
windows 38104
13.8%
pc 38104
13.8%
atm 37942
13.7%
machine 37942
13.7%
mac 31173
11.3%
android 31016
11.2%
linux 30969
11.2%
iphone 30796
11.2%

Most occurring characters

ValueCountFrequency (%)
i 168827
 
11.7%
n 168827
 
11.7%
M 107057
 
7.4%
d 100136
 
6.9%
o 99916
 
6.9%
76046
 
5.2%
c 69115
 
4.8%
a 69115
 
4.8%
A 68958
 
4.8%
P 68900
 
4.8%
Other values (11) 451757
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1448654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 168827
 
11.7%
n 168827
 
11.7%
M 107057
 
7.4%
d 100136
 
6.9%
o 99916
 
6.9%
76046
 
5.2%
c 69115
 
4.8%
a 69115
 
4.8%
A 68958
 
4.8%
P 68900
 
4.8%
Other values (11) 451757
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1448654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 168827
 
11.7%
n 168827
 
11.7%
M 107057
 
7.4%
d 100136
 
6.9%
o 99916
 
6.9%
76046
 
5.2%
c 69115
 
4.8%
a 69115
 
4.8%
A 68958
 
4.8%
P 68900
 
4.8%
Other values (11) 451757
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1448654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 168827
 
11.7%
n 168827
 
11.7%
M 107057
 
7.4%
d 100136
 
6.9%
o 99916
 
6.9%
76046
 
5.2%
c 69115
 
4.8%
a 69115
 
4.8%
A 68958
 
4.8%
P 68900
 
4.8%
Other values (11) 451757
31.2%

Is_Fraud
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.1 MiB
0
186000 
1
 
14000

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters200000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 186000
93.0%
1 14000
 
7.0%

Length

2025-02-26T21:22:24.653040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T21:22:24.719656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 186000
93.0%
1 14000
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 186000
93.0%
1 14000
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 186000
93.0%
1 14000
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 186000
93.0%
1 14000
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 186000
93.0%
1 14000
 
7.0%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.4 MiB
USD
160082 
CAD
19978 
GBP
 
10009
EUR
 
9931

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters600000
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowEUR
4th rowCAD
5th rowCAD

Common Values

ValueCountFrequency (%)
USD 160082
80.0%
CAD 19978
 
10.0%
GBP 10009
 
5.0%
EUR 9931
 
5.0%

Length

2025-02-26T21:22:24.786450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T21:22:24.853017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
usd 160082
80.0%
cad 19978
 
10.0%
gbp 10009
 
5.0%
eur 9931
 
5.0%

Most occurring characters

ValueCountFrequency (%)
D 180060
30.0%
U 170013
28.3%
S 160082
26.7%
C 19978
 
3.3%
A 19978
 
3.3%
G 10009
 
1.7%
B 10009
 
1.7%
P 10009
 
1.7%
E 9931
 
1.7%
R 9931
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 600000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 180060
30.0%
U 170013
28.3%
S 160082
26.7%
C 19978
 
3.3%
A 19978
 
3.3%
G 10009
 
1.7%
B 10009
 
1.7%
P 10009
 
1.7%
E 9931
 
1.7%
R 9931
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 600000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 180060
30.0%
U 170013
28.3%
S 160082
26.7%
C 19978
 
3.3%
A 19978
 
3.3%
G 10009
 
1.7%
B 10009
 
1.7%
P 10009
 
1.7%
E 9931
 
1.7%
R 9931
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 600000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 180060
30.0%
U 170013
28.3%
S 160082
26.7%
C 19978
 
3.3%
A 19978
 
3.3%
G 10009
 
1.7%
B 10009
 
1.7%
P 10009
 
1.7%
E 9931
 
1.7%
R 9931
 
1.7%
Distinct197504
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size13.0 MiB
2025-02-26T21:22:25.265240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters2200000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195021 ?
Unique (%)97.5%

Sample

1st row+1-240-2997
2nd row+1-641-1940
3rd row+1-685-4317
4th row+1-650-5207
5th row+1-995-5816
ValueCountFrequency (%)
1-349-4074 3
 
< 0.1%
1-622-6452 3
 
< 0.1%
1-973-1511 3
 
< 0.1%
1-593-3288 3
 
< 0.1%
1-245-1873 3
 
< 0.1%
1-959-4796 3
 
< 0.1%
1-560-9175 3
 
< 0.1%
1-438-3247 3
 
< 0.1%
1-112-1104 3
 
< 0.1%
1-911-8493 3
 
< 0.1%
Other values (197494) 199970
> 99.9%
2025-02-26T21:22:25.836654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 400000
18.2%
1 345174
15.7%
+ 200000
9.1%
5 144627
 
6.6%
7 144595
 
6.6%
3 144571
 
6.6%
2 144533
 
6.6%
8 144338
 
6.6%
6 144288
 
6.6%
4 143979
 
6.5%
Other values (2) 243895
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2200000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 400000
18.2%
1 345174
15.7%
+ 200000
9.1%
5 144627
 
6.6%
7 144595
 
6.6%
3 144571
 
6.6%
2 144533
 
6.6%
8 144338
 
6.6%
6 144288
 
6.6%
4 143979
 
6.5%
Other values (2) 243895
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2200000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 400000
18.2%
1 345174
15.7%
+ 200000
9.1%
5 144627
 
6.6%
7 144595
 
6.6%
3 144571
 
6.6%
2 144533
 
6.6%
8 144338
 
6.6%
6 144288
 
6.6%
4 143979
 
6.5%
Other values (2) 243895
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2200000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 400000
18.2%
1 345174
15.7%
+ 200000
9.1%
5 144627
 
6.6%
7 144595
 
6.6%
3 144571
 
6.6%
2 144533
 
6.6%
8 144338
 
6.6%
6 144288
 
6.6%
4 143979
 
6.5%
Other values (2) 243895
11.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.4 MiB
Refund
40205 
Gift
40047 
Bill Payment
40039 
Subscription
39916 
Payment
39793 

Length

Max length12
Median length7
Mean length8.197145
Min length4

Characters and Unicode

Total characters1639429
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGift
2nd rowPayment
3rd rowSubscription
4th rowSubscription
5th rowGift

Common Values

ValueCountFrequency (%)
Refund 40205
20.1%
Gift 40047
20.0%
Bill Payment 40039
20.0%
Subscription 39916
20.0%
Payment 39793
19.9%

Length

2025-02-26T21:22:26.187611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T21:22:26.294584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
payment 79832
33.3%
refund 40205
16.7%
gift 40047
16.7%
bill 40039
16.7%
subscription 39916
16.6%

Most occurring characters

ValueCountFrequency (%)
n 159953
 
9.8%
i 159918
 
9.8%
t 159795
 
9.7%
e 120037
 
7.3%
f 80252
 
4.9%
u 80121
 
4.9%
l 80078
 
4.9%
y 79832
 
4.9%
a 79832
 
4.9%
m 79832
 
4.9%
Other values (13) 559779
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1639429
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 159953
 
9.8%
i 159918
 
9.8%
t 159795
 
9.7%
e 120037
 
7.3%
f 80252
 
4.9%
u 80121
 
4.9%
l 80078
 
4.9%
y 79832
 
4.9%
a 79832
 
4.9%
m 79832
 
4.9%
Other values (13) 559779
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1639429
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 159953
 
9.8%
i 159918
 
9.8%
t 159795
 
9.7%
e 120037
 
7.3%
f 80252
 
4.9%
u 80121
 
4.9%
l 80078
 
4.9%
y 79832
 
4.9%
a 79832
 
4.9%
m 79832
 
4.9%
Other values (13) 559779
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1639429
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 159953
 
9.8%
i 159918
 
9.8%
t 159795
 
9.7%
e 120037
 
7.3%
f 80252
 
4.9%
u 80121
 
4.9%
l 80078
 
4.9%
y 79832
 
4.9%
a 79832
 
4.9%
m 79832
 
4.9%
Other values (13) 559779
34.1%
Distinct8999
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size14.9 MiB
2025-02-26T21:22:26.504016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters4200000
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcustomer2148@bank.com
2nd rowcustomer8393@bank.com
3rd rowcustomer8594@bank.com
4th rowcustomer1396@bank.com
5th rowcustomer1126@bank.com
ValueCountFrequency (%)
customer5223@bank.com 44
 
< 0.1%
customer6842@bank.com 43
 
< 0.1%
customer4526@bank.com 43
 
< 0.1%
customer7358@bank.com 42
 
< 0.1%
customer7532@bank.com 41
 
< 0.1%
customer6718@bank.com 40
 
< 0.1%
customer4530@bank.com 40
 
< 0.1%
customer1298@bank.com 40
 
< 0.1%
customer2562@bank.com 39
 
< 0.1%
customer3356@bank.com 39
 
< 0.1%
Other values (8989) 199589
99.8%
2025-02-26T21:22:26.788426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 400000
 
9.5%
o 400000
 
9.5%
m 400000
 
9.5%
u 200000
 
4.8%
t 200000
 
4.8%
s 200000
 
4.8%
e 200000
 
4.8%
r 200000
 
4.8%
@ 200000
 
4.8%
n 200000
 
4.8%
Other values (14) 1600000
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4200000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 400000
 
9.5%
o 400000
 
9.5%
m 400000
 
9.5%
u 200000
 
4.8%
t 200000
 
4.8%
s 200000
 
4.8%
e 200000
 
4.8%
r 200000
 
4.8%
@ 200000
 
4.8%
n 200000
 
4.8%
Other values (14) 1600000
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4200000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 400000
 
9.5%
o 400000
 
9.5%
m 400000
 
9.5%
u 200000
 
4.8%
t 200000
 
4.8%
s 200000
 
4.8%
e 200000
 
4.8%
r 200000
 
4.8%
@ 200000
 
4.8%
n 200000
 
4.8%
Other values (14) 1600000
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4200000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 400000
 
9.5%
o 400000
 
9.5%
m 400000
 
9.5%
u 200000
 
4.8%
t 200000
 
4.8%
s 200000
 
4.8%
e 200000
 
4.8%
r 200000
 
4.8%
@ 200000
 
4.8%
n 200000
 
4.8%
Other values (14) 1600000
38.1%

Interactions

2025-02-26T21:22:15.056242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-26T21:22:14.055809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-26T21:22:14.551529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-26T21:22:15.211317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-26T21:22:14.221494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-26T21:22:14.719905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-26T21:22:15.363921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-26T21:22:14.382918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-26T21:22:14.877777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-26T21:22:26.899299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Account_BalanceAccount_TypeAgeBank_BranchCityDevice_TypeGenderIs_FraudMerchant_CategoryStateTransaction_AmountTransaction_CurrencyTransaction_DescriptionTransaction_DeviceTransaction_LocationTransaction_Type
Account_Balance1.0000.000-0.0000.0030.0020.0010.0030.0040.0010.0020.0010.0030.0030.0000.0020.000
Account_Type0.0001.0000.0040.0030.0000.0020.0000.0000.0000.0000.0030.0020.0020.0000.0000.002
Age-0.0000.0041.0000.0000.0000.0020.0040.0000.0010.0020.0000.0020.0000.0000.0000.001
Bank_Branch0.0030.0030.0001.0000.0030.0010.0000.0000.0000.0000.0000.0050.0020.0020.0030.003
City0.0020.0000.0000.0031.0000.0000.0050.0000.0000.0000.0010.0000.0000.0040.0000.000
Device_Type0.0010.0020.0020.0010.0001.0000.0000.3500.0400.0000.1570.0000.0000.0520.1570.000
Gender0.0030.0000.0040.0000.0050.0001.0000.0000.0030.0030.0030.0030.0000.0000.0040.000
Is_Fraud0.0040.0000.0000.0000.0000.3500.0001.0000.2560.0011.0000.0000.0030.2551.0000.000
Merchant_Category0.0010.0000.0010.0000.0000.0400.0030.2561.0000.0010.1140.0000.0000.0380.1140.000
State0.0020.0000.0020.0000.0000.0000.0030.0010.0011.0000.0000.0000.0040.0040.0030.000
Transaction_Amount0.0010.0030.0000.0000.0010.1570.0031.0000.1140.0001.0000.0000.0000.1470.3540.000
Transaction_Currency0.0030.0020.0020.0050.0000.0000.0030.0000.0000.0000.0001.0000.0000.0000.0030.000
Transaction_Description0.0030.0020.0000.0020.0000.0000.0000.0030.0000.0040.0000.0001.0000.0000.0020.003
Transaction_Device0.0000.0000.0000.0020.0040.0520.0000.2550.0380.0040.1470.0000.0001.0000.1470.003
Transaction_Location0.0020.0000.0000.0030.0000.1570.0041.0000.1140.0030.3540.0030.0020.1471.0000.004
Transaction_Type0.0000.0020.0010.0030.0000.0000.0000.0000.0000.0000.0000.0000.0030.0030.0041.000

Missing values

2025-02-26T21:22:15.761725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-26T21:22:16.378929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Customer_IDCustomer_NameGenderAgeStateCityBank_BranchAccount_TypeTransaction_IDTransaction_DateTransaction_TimeTransaction_AmountMerchant_IDTransaction_TypeMerchant_CategoryAccount_BalanceTransaction_DeviceTransaction_LocationDevice_TypeIs_FraudTransaction_CurrencyCustomer_ContactTransaction_DescriptionCustomer_Email
0CUST000001Customer_1Male25CALos AngelesBranch BCheckingTXN0000000012023-01-01 00:00:0000:00:00102.690676MERCHANT839OnlineClothing1760.420524POS TerminalCAWindows PC0USD+1-240-2997Giftcustomer2148@bank.com
1CUST000002Customer_2Female58FLNew YorkBranch CSavingsTXN0000000022023-01-01 00:01:0000:01:0083.034624MERCHANT50ATM WithdrawalLuxury7833.088780WebTXAndroid0USD+1-641-1940Paymentcustomer8393@bank.com
2CUST000003Customer_3Female71PADallasBranch ASavingsTXN0000000032023-01-01 00:02:0000:02:0051.521278MERCHANT453ATM WithdrawalTravel7239.890561POS TerminalILWindows PC0EUR+1-685-4317Subscriptioncustomer8594@bank.com
3CUST000004Customer_4Female27NYChicagoBranch ASavingsTXN0000000042023-01-01 00:03:0000:03:0074.897156MERCHANT231ATM WithdrawalClothing4269.744125POS TerminalCAWindows PC0CAD+1-650-5207Subscriptioncustomer1396@bank.com
4CUST000005Customer_5Male28OHPhiladelphiaBranch BSavingsTXN0000000052023-01-01 00:04:0000:04:005.000000MERCHANT664OnlineElectronics4877.511195WebPALinux0CAD+1-995-5816Giftcustomer1126@bank.com
5CUST000006Customer_6Male35ILPhiladelphiaBranch BCheckingTXN0000000062023-01-01 00:05:0000:05:0064.265487MERCHANT571OnlineLuxury6608.633036ATMILMac0USD+1-427-5502Refundcustomer4636@bank.com
6CUST000007Customer_7Male49TXMiamiBranch BSavingsTXN0000000072023-01-01 00:06:0000:06:0033.407848MERCHANT49ATM WithdrawalLuxury4070.596903POS TerminalILWindows PC0USD+1-534-6600Subscriptioncustomer8995@bank.com
7CUST000008Customer_8Female33PALos AngelesBranch DBusinessTXN0000000082023-01-01 00:07:0000:07:0021.495652MERCHANT767OnlineTravel4790.814115POS TerminalOHATM Machine0USD+1-676-2700Paymentcustomer2414@bank.com
8CUST000009Customer_9Female74OHHoustonBranch ASavingsTXN0000000092023-01-01 00:08:0000:08:0031.181209MERCHANT203ATM WithdrawalGrocery2799.823637WebCALinux0USD+1-265-6726Bill Paymentcustomer6549@bank.com
9CUST000010Customer_10Female37FLPhiladelphiaBranch DSavingsTXN0000000102023-01-01 00:09:0000:09:0021.235605MERCHANT550OnlineTravel3511.964463POS TerminalTXiPhone0USD+1-546-6429Subscriptioncustomer4352@bank.com
Customer_IDCustomer_NameGenderAgeStateCityBank_BranchAccount_TypeTransaction_IDTransaction_DateTransaction_TimeTransaction_AmountMerchant_IDTransaction_TypeMerchant_CategoryAccount_BalanceTransaction_DeviceTransaction_LocationDevice_TypeIs_FraudTransaction_CurrencyCustomer_ContactTransaction_DescriptionCustomer_Email
199990CUST199991Customer_199991Female31FLChicagoBranch DSavingsTXN0001999912023-05-19 21:10:0021:10:00713.527806MERCHANT724POSLuxury5026.085810WebOfWindows PC1USD+1-928-9568Subscriptioncustomer6942@bank.com
199991CUST199992Customer_199992Female75OHChicagoBranch CBusinessTXN0001999922023-05-19 21:11:0021:11:00729.985845MERCHANT32POSLuxury8269.853573WebOfWindows PC1USD+1-911-4172Subscriptioncustomer6191@bank.com
199992CUST199993Customer_199993Male69ILDallasBranch CSavingsTXN0001999932023-05-19 21:12:0021:12:00805.121697MERCHANT947POSOnline Services7300.610412WebHiWindows PC1EUR+1-244-4245Bill Paymentcustomer4778@bank.com
199993CUST199994Customer_199994Male44TXDallasBranch CSavingsTXN0001999942023-05-19 21:13:0021:13:00500.000000MERCHANT21POSTravel5084.616462ATMOfATM Machine1EUR+1-766-3547Giftcustomer1396@bank.com
199994CUST199995Customer_199995Male26OHMiamiBranch DBusinessTXN0001999952023-05-19 21:14:0021:14:00802.320096MERCHANT436POSLuxury5592.085149WebUnATM Machine1CAD+1-696-4653Paymentcustomer2891@bank.com
199995CUST199996Customer_199996Female50NYMiamiBranch DSavingsTXN0001999962023-05-19 21:15:0021:15:00527.962149MERCHANT32OnlineOnline Services4465.649560ATMHiWindows PC1USD+1-103-6246Paymentcustomer9284@bank.com
199996CUST199997Customer_199997Female69PANew YorkBranch BSavingsTXN0001999972023-05-19 21:16:0021:16:00568.070220MERCHANT585ATM WithdrawalLuxury2164.251235ATMHiWindows PC1CAD+1-456-3560Subscriptioncustomer8558@bank.com
199997CUST199998Customer_199998Male23FLDallasBranch ABusinessTXN0001999982023-05-19 21:17:0021:17:001178.822244MERCHANT669POSLuxury1739.432350WebHiWindows PC1USD+1-337-4670Refundcustomer7015@bank.com
199998CUST199999Customer_199999Female36FLPhiladelphiaBranch BCheckingTXN0001999992023-05-19 21:18:0021:18:001008.643360MERCHANT677OnlineOnline Services4797.744120ATMHiATM Machine1USD+1-537-8714Subscriptioncustomer2544@bank.com
199999CUST200000Customer_200000Male36PADallasBranch DSavingsTXN0002000002023-05-19 21:19:0021:19:00789.449699MERCHANT805POSOnline Services3521.334811WebOfWindows PC1USD+1-970-5789Subscriptioncustomer3550@bank.com